A decision tree has been used as one of a classification approach of input data. The decision tree is a decision support tool that uses a tree-like model of decisions and possible consequences, including branch conditions (nodes) which classify input data hierarchically. In these tree structures, leaves represent classifications and branches represent conjunctions of features that lead to those classifications.
It has been suggested as a way of learning the decision tree, that learning samples already given identified classes are classified recursively by using the decision tree to find branch conditions of the decision tree written in a memory. U.S. Pat. No. 7,310,624, for example, has proposed a method of classifying data. In the above-mentioned method, a data inputted into the root node (top node) is moved from a parent node to a child node in accordance with the branch conditions.
In a discriminating process using the decision tree, transitions between nodes occur frequently. Thereby it becomes necessary to access an address where each node is held in the memory, which tends to cause accessing addresses being away from each other. Such memory accesses lead to a decrease in a cache hit ratio and cause slowdowns of processes.